1. Field of the Invention
This invention relates generally to the surveillance of one or more objects over a surveillance area. More particularly, it relates to methods and apparatus for the generic extraction and compression of surveillance data acquired from multiple sensors operating over a surveillance area that facilitate the fusion of such data into more useful or otherwise actionable information.
2. Background of the Invention
Multi-sensor surveillance systems and methods are receiving significant attention for both military and nonmilitary applications due, in part, to a number of operational benefits provided by such systems and methods. In particular, some of the benefits provided by multi-sensor systems include: Robust operational performance is provided because any one particular sensor of the multi-sensor system has the potential to contribute information while others are unavailable, denied (jammed), or lacking coverage of an event or target; Extended spatial coverage is provided because one sensor can “look” where another sensor cannot; Extended temporal coverage is provided because one sensor can detect or measure at times that others cannot; Increased confidence is accrued when multiple independent measurements are made on the same event or target; Reduced ambiguity in measured information is achieved when the information provided by multiple sensors reduces the set of hypothesis about a target or event; Improved detection performance results from the effective integration of multiple, separate measurements of the same event or target; Increased system operational reliability may result from the inherent redundancy of a multi-sensor suite; and Increased dimensionality of a measurement space (i.e., different sensors measuring various portions of the electro-magnetic spectrum) reduces vulnerability to denial (countermeasures, jamming, weather, noise) of any single portion of the measurement space.
These benefits, however, do not come without a price. The overwhelming volume and complexity of the disparate data and information produced by multi-sensor systems is well beyond the ability of humans to process, analyze and render decisions in a reasonable amount of time. Consequently, data fusion technologies are being developed to help combine various data and information structures into form(s) that are more convenient and useful to human operators.
Briefly stated, data fusion involves the acquisition, filtering, correlation and integration of relevant data and/or information from various sources, such as multi-sensor surveillance systems, databases, or knowledge bases into one or more formats appropriate for deriving decisions, system goals (i.e., recognition, tracking, or situation assessment), sensor management or system control. The objective of data fusion is the maximization of useful information, such that the fused information provides a more detailed representation with less uncertainty than that obtained from individual source(s). While producing more valuable information, the fusion process may also allow for a more efficient representation of the data and may further permit the observation of higher-order relationships between respective data entities.
Current systems and methods for multi-sensor surveillance have typically utilized sensor platforms or “node level solutions” that employ relatively powerful processors to determine the bulk of a target classification and tracking solution at a local surveillance node level. Typical sensor data fusion approaches in distributed sensor systems are low performance, and could be more accurately described as systems that share “pre-processed” data generated at the node level (such as target classification, range, or bearing).
There is a tendency to design system solutions in this manner in order to reduce the data transmission requirements between nodes or from the nodes to a central processor. Such system approaches have been difficult to develop and are not inherently flexible because of constant upgrades to node level processing and custom system level data fusion, which is inextricably related to custom hardware/software within the node. Accordingly, efficient data collection and high performance data fusion has not been realized in distributed sensor systems as a result of the inability to define a suitably flexible system solution and the inability to collect all sensor information from multiple sensor sites. Accordingly, systems and methods that provide multi-sensor surveillance, while simultaneously facilitating the data fusion from these sensors, are of great interest.